Impact of population based indoor residual spraying with and without mass drug administration with dihydroartemisinin-piperaquine on malaria prevalence in a high transmission setting: a quasi-experimental controlled before-and-after trial in northeastern Uganda

Background Declines in malaria burden in Uganda have slowed. Modelling predicts that indoor residual spraying (IRS) and mass drug administration (MDA), when co-timed, have synergistic impact. This study investigated additional protective impact of population-based MDA on malaria prevalence, if any, when added to IRS, as compared with IRS alone and with standard of care (SOC). Methods The 32-month quasi-experimental controlled before-and-after trial enrolled an open cohort of residents (46,765 individuals, 1st enumeration and 52,133, 4th enumeration) of Katakwi District in northeastern Uganda. Consented participants were assigned to three arms based on residential subcounty at study start: MDA+IRS, IRS, SOC. IRS with pirimiphos methyl and MDA with dihydroartemisinin- piperaquine were delivered in 4 co-timed campaign-style rounds 8 months apart. The primary endpoint was population prevalence of malaria, estimated by 6 cross-sectional surveys, starting at baseline and preceding each subsequent round. Results Comparing malaria prevalence in MDA+IRS and IRS only arms over all 6 surveys (intention-to-treat analysis), roughly every 6 months post-interventions, a geostatistical model found a significant additional 15.5% (95% confidence interval (CI): [13.7%, 17.5%], Z = 9.6, p = 5e−20) decrease in the adjusted odds ratio (aOR) due to MDA for all ages, a 13.3% reduction in under 5’s (95% CI: [10.5%, 16.8%], Z = 4.02, p = 5e−5), and a 10.1% reduction in children 5–15 (95% CI: [8.5%, 11.8%], Z = 4.7, p = 2e−5). All ages residents of the MDA + IRS arm enjoyed an overall 80.1% reduction (95% CI: [80.0%, 83.0%], p = 0.0001) in odds of qPCR confirmed malaria compared with SOC residents. Secondary difference-in-difference analyses comparing surveys at different timepoints to baseline showed aOR (MDA + IRS vs IRS) of qPCR positivity between 0.28 and 0.66 (p < 0.001). Of three serious adverse events, one (nonfatal) was considered related to study medications. Limitations include the initial non-random assignment of study arms, the single large cluster per arm, and the lack of an MDA-only arm, considered to violate equipoise. Conclusions Despite being assessed at long time points 5–7 months post-round, MDA plus IRS provided significant additional protection from malaria infection over IRS alone. Randomized trials of MDA in large areas undergoing IRS recommended as well as cohort studies of impact on incidence. Trial registration: This trial was retrospectively registered 11/07/2018 with the Pan African Clinical Trials Registry (PACTR201807166695568). Supplementary Information The online version contains supplementary material available at 10.1186/s12879-023-07991-w.

In this expression, a ij denotes the age of person j at location x i , and a 0 = 10 and a 1 = 29 are ages where a change in the slope of the linear spline is imposed. The values of 10 and 29 years were chosen based on a deviance profile of all possible combinations for a 0 and a 1 using a standard generalized linear model. In the above equation, β 1 , β 2 and β 3 are the slopes of age in different age groups, that is the effects of age on the log-odds of prevalence in the age groups 0 − 10, 10 − 29, and 30+ respectively.
We used linear functions of time (corresponding to β 4 ) to account for long-term trends. Seasonality was included by classifying the year into a high transmission season (June-August), with prefactor β 5 and low transmission season otherwise.
We accounted for differences in prevalence between different trial arms at baseline by including indicator variables of whether or not a data point was sampled at baseline in (Baseline), Kapujan during baseline (Kap base ) and Toroma during baseline (T or base ) corresponding to β 6 β 7 , β 8 , respectively.
To account for the different intervention strategies, T or represents Toroma, Arm B, which received IRS only while Kap represents Arm A, Kapujan, which received both IRS and MDA. The main regression parameters of interest are therefore α 1 , α 2 , respectively, which when lesser, represents a greater impact of the corresponding intervention. Arm C, Magoro, the SOC arm, is the reference category. We modelled the spatially and temporally correlated random variations S(x i , t i ) as a stationary and isotropic Gaussian process with the separable correlation function, where the spatial component of the correlation function is modelled as ρ 1 (u; (σ 2 , ϕ)) = exp(−u/ϕ), where ϕ regulates the pace at which the spatial correlation decays for increasing distance u between any two locations, and the temporal correlation function as ρ 2 (v; ψ) = 1/(1 + v/ψ) with v being the difference between any two time points. We modelled the spatially unstructured random effects Z(x i , t i ) as independent and identically distributed Gaussian random variables with variance τ 2 .
The final data consisted of 15925 qPCR tests for P. falciparum, of which 6730 (42.3%) were positive. Table 1 shows parameter estimates of the geostatistical model of equations 1-2. As expected, the odds ratio increases sharply in the early years of life until age 10, and it decreases moderately thereafter until around the age of 30, after which there is very slow decrease in later years (Fig. 1). The estimate range of the spatial correlation is about 1.0km, beyond which the spatial correlation takes values smaller than 0.05. The estimated range of the temporal correlation is about one month. As indicated by the larger estimate of σ 2 than τ 2 , the spatial variation dominates substantially the unexplained variation on a scale smaller than the minimum observed distance.  Figure 1 The effect of age on odds ratio. The solid line indicates the odds ratio at different ages, and the shaded grey area is the associated 95% confidence interval.

Appendix II. Additional DiD Analyses
The DiD design is a quasi-experimental method that relies on the assumption that in the absence of any intervention, the study arms receiving the enhanced malaria interventions have identical trends in outcomes as the SOC (control) arm.
Using SAS 9.4, the model used a binomial logistic regression with the proportion of positive malaria cases by diagnostic tool (qPCR, microscopy and RDT) and age as the dependable variables. Indicator variables for survey timing (baseline/postintervention surveys) and study arms were used, with co-variates determined through backwards stepwise procedure, and an interaction term between survey timing and study arms. Statistical significance threshold was set at alpha = .05 using two tailed tests. Log odds were converted to odds ratios by calculating the exponentiated regression coefficient of the interaction terms in the model. DiD analyses were not done for positive malaria cases in the 'all ages' group by RDT, since malaria prevalence at baseline was significantly higher in the SoC (Arm C) compared to Arms A and B by this metric.
After adjusting for age, ITN use and gender, Fig. 2  Residents in the IRS arm (Fig. 2,upper right) also showed significantly lower odds of qPCR confirmed malaria in relation to SoC residents. All surveys measured impact 5 -7 months following intervention rounds, with the exception of survey 3, which showed significant impact in residents receiving MDA+IRS 3 months after receiving 2 rounds of the intervention compared to residents receiving SoC (DiD aOR = .11 [95% CI: .07 -.15], p< .001) and residents receiving IRS (DiD aOR = .30 [95% CI: .21 -.41], p< .001). The lower panels of Fig. 2 show the same trends in microscopy confirmed malaria for all ages.
These comparisons highlight the large protective impact of not only adding chemoprevention to IRS, but also the protective impact IRS+ITNs have over ITNs alone in this high transmission setting.